Overview

Dataset statistics

Number of variables12
Number of observations834
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.5 KiB
Average record size in memory154.1 B

Variable types

Numeric10
Categorical2

Alerts

Anio is highly overall correlated with + 30 Mbps and 1 other fieldsHigh correlation
+ 1 Mbps - 6 Mbps is highly overall correlated with + 6 Mbps - 10 Mbps and 1 other fieldsHigh correlation
+ 6 Mbps - 10 Mbps is highly overall correlated with + 1 Mbps - 6 Mbps and 1 other fieldsHigh correlation
+ 10 Mbps - 20 Mbps is highly overall correlated with TotalHigh correlation
+ 30 Mbps is highly overall correlated with Anio and 2 other fieldsHigh correlation
OTROS is highly overall correlated with Anio and 1 other fieldsHigh correlation
Total is highly overall correlated with + 1 Mbps - 6 Mbps and 4 other fieldsHigh correlation
Provincia is highly overall correlated with TotalHigh correlation
Provincia is uniformly distributedUniform
+ 512 Kbps - 1 Mbps has 50 (6.0%) zerosZeros
+ 6 Mbps - 10 Mbps has 38 (4.6%) zerosZeros
+ 10 Mbps - 20 Mbps has 71 (8.5%) zerosZeros
+ 20 Mbps - 30 Mbps has 104 (12.5%) zerosZeros
+ 30 Mbps has 112 (13.4%) zerosZeros
OTROS has 449 (53.8%) zerosZeros

Reproduction

Analysis started2023-07-16 01:56:46.377635
Analysis finished2023-07-16 01:56:57.731138
Duration11.35 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Anio
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8777
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:57.809628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5360605
Coefficient of variation (CV)0.0012567959
Kurtosis-1.2118077
Mean2017.8777
Median Absolute Deviation (MAD)2
Skewness0.031372512
Sum1682910
Variance6.4316029
MonotonicityDecreasing
2023-07-15T20:56:57.954448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 96
11.5%
2020 96
11.5%
2018 96
11.5%
2017 96
11.5%
2016 96
11.5%
2015 96
11.5%
2014 96
11.5%
2019 90
10.8%
2022 72
8.6%
ValueCountFrequency (%)
2014 96
11.5%
2015 96
11.5%
2016 96
11.5%
2017 96
11.5%
2018 96
11.5%
2019 90
10.8%
2020 96
11.5%
2021 96
11.5%
2022 72
8.6%
ValueCountFrequency (%)
2022 72
8.6%
2021 96
11.5%
2020 96
11.5%
2019 90
10.8%
2018 96
11.5%
2017 96
11.5%
2016 96
11.5%
2015 96
11.5%
2014 96
11.5%

Trimestre
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
3
216 
1
216 
2
210 
4
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters834
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Length

2023-07-15T20:56:58.084791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:56:58.196675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Most occurring characters

ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 834
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common 834
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 216
25.9%
1 216
25.9%
2 210
25.2%
4 192
23.0%

Provincia
Categorical

HIGH CORRELATION  UNIFORM 

Distinct24
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
Buenos Aires
 
35
Formosa
 
35
San Luis
 
35
Catamarca
 
35
Chaco
 
35
Other values (19)
659 

Length

Max length19
Median length15
Mean length8.9052758
Min length5

Characters and Unicode

Total characters7427
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 35
 
4.2%
Formosa 35
 
4.2%
San Luis 35
 
4.2%
Catamarca 35
 
4.2%
Chaco 35
 
4.2%
Chubut 35
 
4.2%
Cordoba 35
 
4.2%
Santa Fe 35
 
4.2%
Entre Rios 35
 
4.2%
Corrientes 35
 
4.2%
Other values (14) 484
58.0%

Length

2023-07-15T20:56:58.330920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 70
 
5.3%
la 69
 
5.2%
santa 69
 
5.2%
del 69
 
5.2%
buenos 35
 
2.7%
juan 35
 
2.7%
salta 35
 
2.7%
negro 35
 
2.7%
rio 35
 
2.7%
neuquen 35
 
2.7%
Other values (24) 832
63.1%

Most occurring characters

ValueCountFrequency (%)
a 1005
13.5%
e 624
 
8.4%
o 557
 
7.5%
485
 
6.5%
n 452
 
6.1%
u 451
 
6.1%
r 451
 
6.1%
i 382
 
5.1%
t 348
 
4.7%
s 315
 
4.2%
Other values (26) 2357
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5623
75.7%
Uppercase Letter 1319
 
17.8%
Space Separator 485
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1005
17.9%
e 624
11.1%
o 557
9.9%
n 452
8.0%
u 451
8.0%
r 451
8.0%
i 382
 
6.8%
t 348
 
6.2%
s 315
 
5.6%
l 172
 
3.1%
Other values (11) 866
15.4%
Uppercase Letter
ValueCountFrequency (%)
C 243
18.4%
S 209
15.8%
F 138
10.5%
L 104
7.9%
R 104
7.9%
N 70
 
5.3%
J 70
 
5.3%
E 70
 
5.3%
M 69
 
5.2%
D 69
 
5.2%
Other values (4) 173
13.1%
Space Separator
ValueCountFrequency (%)
485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6942
93.5%
Common 485
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1005
14.5%
e 624
 
9.0%
o 557
 
8.0%
n 452
 
6.5%
u 451
 
6.5%
r 451
 
6.5%
i 382
 
5.5%
t 348
 
5.0%
s 315
 
4.5%
C 243
 
3.5%
Other values (25) 2114
30.5%
Common
ValueCountFrequency (%)
485
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1005
13.5%
e 624
 
8.4%
o 557
 
7.5%
485
 
6.5%
n 452
 
6.1%
u 451
 
6.1%
r 451
 
6.1%
i 382
 
5.1%
t 348
 
4.7%
s 315
 
4.2%
Other values (26) 2357
31.7%

HASTA 512 kbps
Real number (ℝ)

Distinct371
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126736.69
Minimum1007
Maximum998000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:58.476607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile1533.6
Q110000
median51000
Q3134504.75
95-th percentile558450
Maximum998000
Range996993
Interquartile range (IQR)124504.75

Descriptive statistics

Standard deviation188046.28
Coefficient of variation (CV)1.4837556
Kurtosis5.3816391
Mean126736.69
Median Absolute Deviation (MAD)46000
Skewness2.2914111
Sum1.056984 × 108
Variance3.5361402 × 1010
MonotonicityNot monotonic
2023-07-15T20:56:58.635112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 19
 
2.3%
18000 16
 
1.9%
15000 16
 
1.9%
10000 15
 
1.8%
67000 14
 
1.7%
16000 14
 
1.7%
8000 12
 
1.4%
71000 12
 
1.4%
26000 11
 
1.3%
6000 10
 
1.2%
Other values (361) 695
83.3%
ValueCountFrequency (%)
1007 1
 
0.1%
1009 1
 
0.1%
1010 3
 
0.4%
1011 1
 
0.1%
1053 1
 
0.1%
1058 1
 
0.1%
1063 1
 
0.1%
1107 1
 
0.1%
1110 8
1.0%
1119 1
 
0.1%
ValueCountFrequency (%)
998000 1
 
0.1%
991000 1
 
0.1%
986000 1
 
0.1%
973000 1
 
0.1%
959000 2
0.2%
958000 3
0.4%
852000 1
 
0.1%
847000 1
 
0.1%
840000 1
 
0.1%
791000 1
 
0.1%

+ 512 Kbps - 1 Mbps
Real number (ℝ)

ZEROS 

Distinct629
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102068.8
Minimum0
Maximum999000
Zeros50
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:58.788789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13366.75
median8774
Q383979.5
95-th percentile579700
Maximum999000
Range999000
Interquartile range (IQR)80612.75

Descriptive statistics

Standard deviation208049.03
Coefficient of variation (CV)2.0383215
Kurtosis6.5810589
Mean102068.8
Median Absolute Deviation (MAD)7391
Skewness2.6464242
Sum85125381
Variance4.3284401 × 1010
MonotonicityNot monotonic
2023-07-15T20:56:58.947771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
6.0%
1000 12
 
1.4%
4000 10
 
1.2%
285000 8
 
1.0%
97000 8
 
1.0%
109000 7
 
0.8%
327000 7
 
0.8%
909000 6
 
0.7%
112000 6
 
0.7%
194000 5
 
0.6%
Other values (619) 715
85.7%
ValueCountFrequency (%)
0 50
6.0%
1000 12
 
1.4%
1047 1
 
0.1%
1058 1
 
0.1%
1062 1
 
0.1%
1077 1
 
0.1%
1099 1
 
0.1%
1123 1
 
0.1%
1164 1
 
0.1%
1169 1
 
0.1%
ValueCountFrequency (%)
999000 1
 
0.1%
995000 1
 
0.1%
991000 1
 
0.1%
987000 2
 
0.2%
974000 1
 
0.1%
940000 2
 
0.2%
928000 2
 
0.2%
909000 6
0.7%
908000 1
 
0.1%
900000 1
 
0.1%

+ 1 Mbps - 6 Mbps
Real number (ℝ)

HIGH CORRELATION 

Distinct825
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151250.41
Minimum2842
Maximum2299705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:59.096797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2842
5-th percentile11896.8
Q128417.75
median48834.5
Q386840.25
95-th percentile737704.75
Maximum2299705
Range2296863
Interquartile range (IQR)58422.5

Descriptive statistics

Standard deviation349293.12
Coefficient of variation (CV)2.3093697
Kurtosis20.943985
Mean151250.41
Median Absolute Deviation (MAD)25713
Skewness4.4452645
Sum1.2614284 × 108
Variance1.2200568 × 1011
MonotonicityNot monotonic
2023-07-15T20:56:59.246144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35409 3
 
0.4%
14014 3
 
0.4%
22409 2
 
0.2%
28600 2
 
0.2%
30727 2
 
0.2%
58588 2
 
0.2%
40285 2
 
0.2%
290315 1
 
0.1%
84859 1
 
0.1%
110296 1
 
0.1%
Other values (815) 815
97.7%
ValueCountFrequency (%)
2842 1
0.1%
3107 1
0.1%
3179 1
0.1%
3576 1
0.1%
3678 1
0.1%
4386 1
0.1%
5018 1
0.1%
5312 1
0.1%
5366 1
0.1%
6038 1
0.1%
ValueCountFrequency (%)
2299705 1
0.1%
2288772 1
0.1%
2281524 1
0.1%
2279875 1
0.1%
2267852 1
0.1%
2266948 1
0.1%
2253197 1
0.1%
2250898 1
0.1%
2250445 1
0.1%
2214760 1
0.1%

+ 6 Mbps - 10 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct752
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71710.797
Minimum0
Maximum917000
Zeros38
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:59.386633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1022.1
Q15391.5
median20208
Q362228.5
95-th percentile311495.35
Maximum917000
Range917000
Interquartile range (IQR)56837

Descriptive statistics

Standard deviation140735.5
Coefficient of variation (CV)1.9625427
Kurtosis13.472766
Mean71710.797
Median Absolute Deviation (MAD)16716.5
Skewness3.4927043
Sum59806805
Variance1.9806482 × 1010
MonotonicityNot monotonic
2023-07-15T20:56:59.532109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.6%
2000 12
 
1.4%
1000 4
 
0.5%
11000 4
 
0.5%
655000 3
 
0.4%
15000 3
 
0.4%
1594 2
 
0.2%
16000 2
 
0.2%
1781 2
 
0.2%
94000 2
 
0.2%
Other values (742) 762
91.4%
ValueCountFrequency (%)
0 38
4.6%
1000 4
 
0.5%
1034 1
 
0.1%
1066 1
 
0.1%
1133 1
 
0.1%
1165 1
 
0.1%
1227 1
 
0.1%
1311 1
 
0.1%
1314 1
 
0.1%
1321 1
 
0.1%
ValueCountFrequency (%)
917000 1
0.1%
902000 1
0.1%
858000 1
0.1%
855000 1
0.1%
849000 1
0.1%
792000 1
0.1%
784000 1
0.1%
779000 1
0.1%
778000 1
0.1%
775000 1
0.1%

+ 10 Mbps - 20 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80226.308
Minimum0
Maximum978000
Zeros71
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:59.677935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14892.25
median15074.5
Q355279
95-th percentile518050
Maximum978000
Range978000
Interquartile range (IQR)50386.75

Descriptive statistics

Standard deviation171019.05
Coefficient of variation (CV)2.1317079
Kurtosis10.120674
Mean80226.308
Median Absolute Deviation (MAD)12478
Skewness3.1626618
Sum66908741
Variance2.9247516 × 1010
MonotonicityNot monotonic
2023-07-15T20:56:59.831091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
8.5%
1000 5
 
0.6%
5000 4
 
0.5%
119000 3
 
0.4%
10000 3
 
0.4%
100000 3
 
0.4%
16000 2
 
0.2%
1292 2
 
0.2%
21452 2
 
0.2%
61000 2
 
0.2%
Other values (711) 737
88.4%
ValueCountFrequency (%)
0 71
8.5%
1000 5
 
0.6%
1061 1
 
0.1%
1062 1
 
0.1%
1076 1
 
0.1%
1085 1
 
0.1%
1162 1
 
0.1%
1172 1
 
0.1%
1203 1
 
0.1%
1215 1
 
0.1%
ValueCountFrequency (%)
978000 1
0.1%
966000 1
0.1%
965000 1
0.1%
958000 1
0.1%
956000 1
0.1%
920000 1
0.1%
888000 1
0.1%
886678 1
0.1%
878000 1
0.1%
832000 1
0.1%

+ 20 Mbps - 30 Mbps
Real number (ℝ)

ZEROS 

Distinct579
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100209.16
Minimum0
Maximum997000
Zeros104
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:56:59.982559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12013
median10930.5
Q363671.25
95-th percentile617150
Maximum997000
Range997000
Interquartile range (IQR)61658.25

Descriptive statistics

Standard deviation208458.88
Coefficient of variation (CV)2.0802377
Kurtosis6.1714277
Mean100209.16
Median Absolute Deviation (MAD)10930.5
Skewness2.6207749
Sum83574442
Variance4.3455103 × 1010
MonotonicityNot monotonic
2023-07-15T20:57:00.138734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
12.5%
1000 24
 
2.9%
5000 18
 
2.2%
2000 13
 
1.6%
3000 8
 
1.0%
4000 7
 
0.8%
22000 5
 
0.6%
29000 5
 
0.6%
17000 4
 
0.5%
30000 4
 
0.5%
Other values (569) 642
77.0%
ValueCountFrequency (%)
0 104
12.5%
1000 24
 
2.9%
1001 1
 
0.1%
1032 1
 
0.1%
1033 1
 
0.1%
1068 1
 
0.1%
1073 1
 
0.1%
1084 1
 
0.1%
1091 1
 
0.1%
1135 1
 
0.1%
ValueCountFrequency (%)
997000 1
0.1%
991000 1
0.1%
979000 1
0.1%
977000 1
0.1%
969000 1
0.1%
964000 1
0.1%
961000 1
0.1%
949093 1
0.1%
941000 1
0.1%
910000 1
0.1%

+ 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct545
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79154.531
Minimum0
Maximum3618689
Zeros112
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:57:00.286829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median882.5
Q319778.25
95-th percentile305674
Maximum3618689
Range3618689
Interquartile range (IQR)19771.25

Descriptive statistics

Standard deviation343530.28
Coefficient of variation (CV)4.3399952
Kurtosis55.689021
Mean79154.531
Median Absolute Deviation (MAD)882.5
Skewness6.9830844
Sum66014879
Variance1.1801306 × 1011
MonotonicityNot monotonic
2023-07-15T20:57:00.618152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
13.4%
2 39
 
4.7%
1 19
 
2.3%
3 15
 
1.8%
4 13
 
1.6%
10 13
 
1.6%
5 9
 
1.1%
13 8
 
1.0%
22 8
 
1.0%
9 7
 
0.8%
Other values (535) 591
70.9%
ValueCountFrequency (%)
0 112
13.4%
1 19
 
2.3%
2 39
 
4.7%
3 15
 
1.8%
4 13
 
1.6%
5 9
 
1.1%
6 1
 
0.1%
7 6
 
0.7%
8 5
 
0.6%
9 7
 
0.8%
ValueCountFrequency (%)
3618689 1
0.1%
3535757 1
0.1%
3381049 1
0.1%
3259793 1
0.1%
2482266 1
0.1%
2337604 1
0.1%
2246313 1
0.1%
2176242 1
0.1%
2085815 1
0.1%
1894466 1
0.1%

OTROS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct315
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32937.064
Minimum-1945
Maximum923000
Zeros449
Zeros (%)53.8%
Negative2
Negative (%)0.2%
Memory size6.6 KiB
2023-07-15T20:57:00.768184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1945
5-th percentile0
Q10
median0
Q37497.75
95-th percentile116380.7
Maximum923000
Range924945
Interquartile range (IQR)7497.75

Descriptive statistics

Standard deviation128937.91
Coefficient of variation (CV)3.9146752
Kurtosis24.912095
Mean32937.064
Median Absolute Deviation (MAD)0
Skewness4.9926264
Sum27469511
Variance1.6624983 × 1010
MonotonicityNot monotonic
2023-07-15T20:57:00.914109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 449
53.8%
2151 6
 
0.7%
6105 5
 
0.6%
1035 5
 
0.6%
4500 3
 
0.4%
4641 3
 
0.4%
36917 3
 
0.4%
792000 3
 
0.4%
2680 3
 
0.4%
3719 3
 
0.4%
Other values (305) 351
42.1%
ValueCountFrequency (%)
-1945 1
 
0.1%
-1000 1
 
0.1%
0 449
53.8%
1000 1
 
0.1%
1035 5
 
0.6%
1123 1
 
0.1%
1144 1
 
0.1%
1246 1
 
0.1%
1305 2
 
0.2%
1313 1
 
0.1%
ValueCountFrequency (%)
923000 1
 
0.1%
898000 1
 
0.1%
895000 1
 
0.1%
833000 1
 
0.1%
803000 1
 
0.1%
793000 1
 
0.1%
792000 3
0.4%
758000 1
 
0.1%
735000 2
0.2%
698000 3
0.4%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct828
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344142.76
Minimum12406
Maximum4721668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-07-15T20:57:01.054183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12406
5-th percentile25694.25
Q152449.75
median104333
Q3177515.5
95-th percentile1414852.6
Maximum4721668
Range4709262
Interquartile range (IQR)125065.75

Descriptive statistics

Standard deviation738791.4
Coefficient of variation (CV)2.1467585
Kurtosis14.691053
Mean344142.76
Median Absolute Deviation (MAD)56379
Skewness3.7625609
Sum2.8701506 × 108
Variance5.4581273 × 1011
MonotonicityNot monotonic
2023-07-15T20:57:01.200357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35710 3
 
0.4%
14029 3
 
0.4%
33772 2
 
0.2%
68538 2
 
0.2%
4721668 1
 
0.1%
106093 1
 
0.1%
80197 1
 
0.1%
637473 1
 
0.1%
88093 1
 
0.1%
180310 1
 
0.1%
Other values (818) 818
98.1%
ValueCountFrequency (%)
12406 1
0.1%
12557 1
0.1%
12741 1
0.1%
13040 1
0.1%
13055 1
0.1%
13147 1
0.1%
13220 1
0.1%
13302 1
0.1%
13488 1
0.1%
13660 1
0.1%
ValueCountFrequency (%)
4721668 1
0.1%
4667183 1
0.1%
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3971683 1
0.1%
3937277 1
0.1%

Interactions

2023-07-15T20:56:56.402989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:46.730988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.848832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.924713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.969966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.987967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.019658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.065868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.270228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.383140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.515318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:46.852273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.966063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.037897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.085255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.123125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.136898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.181989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.388515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.497950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.617159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:46.961348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.072565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.148099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.186755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.223560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.243971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.286664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.498151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.600893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.717926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.070894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.186697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.248703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.286453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.327799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.349487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.388643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.606463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.702171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.817867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.177631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.289486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.351813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.385935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.423542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.451111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.488678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.710193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.801050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.913498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.289404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.393006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.454076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.482174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.516953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.549628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.755084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.810457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.897199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:57.012147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.399119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.495938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.555983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.585151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.619910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.652611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.859283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.918799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.998164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:57.115527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.512236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.600452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.661166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.687026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.719553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.754525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:53.962420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.026588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.101815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:57.222335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.627687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.711938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.770541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.796123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.832043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.867762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.072788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.139530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.211565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:57.318680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:47.737004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:48.817919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:49.870961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:50.893703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:51.925824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:52.965830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:54.172532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:55.277712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-15T20:56:56.306169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-15T20:57:01.302692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AnioHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotalTrimestreProvincia
Anio1.0000.317-0.029-0.1380.1480.1550.3100.7130.7420.3040.0160.000
HASTA 512 kbps0.3171.0000.0520.1020.1900.1220.1210.2450.2770.2280.0000.319
+ 512 Kbps - 1 Mbps-0.0290.0521.0000.2090.1370.161-0.0430.0750.0230.1960.0000.271
+ 1 Mbps - 6 Mbps-0.1380.1020.2091.0000.6110.3620.1470.324-0.0270.7610.0000.397
+ 6 Mbps - 10 Mbps0.1480.1900.1370.6111.0000.4810.3050.4880.1540.6880.0000.322
+ 10 Mbps - 20 Mbps0.1550.1220.1610.3620.4811.0000.2300.4410.2030.5460.0000.210
+ 20 Mbps - 30 Mbps0.3100.121-0.0430.1470.3050.2301.0000.3960.2700.3250.0000.198
+ 30 Mbps0.7130.2450.0750.3240.4880.4410.3961.0000.5690.7250.0000.293
OTROS0.7420.2770.023-0.0270.1540.2030.2700.5691.0000.3200.0000.177
Total0.3040.2280.1960.7610.6880.5460.3250.7250.3201.0000.0000.577
Trimestre0.0160.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
Provincia0.0000.3190.2710.3970.3220.2100.1980.2930.1770.5770.0001.000

Missing values

2023-07-15T20:56:57.460149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-15T20:56:57.651574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AnioTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
020223Buenos Aires29985277092903152979152670441241903618689658214721668
120223Capital Federal51700057423437167829519462869212531051054771547679
220223Catamarca71000384000310753895099373750298220870293
320223Chaco4610009870001678218938804915828793903711144146
420223Chubut1090001444457073094034682153091756320024165778
520223Cordoba99000113121533241116157098927112650344138731038668
620223Corrientes6700038652342723948777621706569507107144846
720223Entre Rios10700055494721046855182633202110219516759268959
820223Formosa97000307000235381954561945640001770458900068538
920223Jujuy580001879191351525436083519000458950118823
AnioTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
82420141Neuquen413398700077148840001582200022083958
82520141Rio Negro467046188430473000106210008094736
82620141Salta530001967764061719231400000091297
82720141San Juan5310002000510560000051589
82820141San Luis70003000125440100002012557
82920141Santa Cruz1610001625249721000100000026760
83020141Santa Fe8456124468345225203286845230006680506013
83120141Santiago Del Estero12341053122817242210900000037113
83220141Tierra Del Fuego12000607000309026000000031527
83320141Tucuman6000346728321011779362000300000130032